Generally, wind farms have to be efficiently and stably operated. To this end, a supervisory control and data acquisition (SCADA) system and a condition monitoring system (CMS) are used as representative key operating technologies.
A SCADA system for operating a wind farm is a computer-based system remotely controlling wind turbines in conjunction with a controller thereof and acquiring data for analyzing and reporting operating performance of the wind turbines, and performs control, monitoring, analysis, and reporting functions.
The SCADA system which has been developed with emphasis on overall operation of a wind farm is focused on monitoring of current operating conditions of individual wind turbines. That is, in order to monitor the individual wind turbines, the SCADA system is focused on acquiring and analyzing representative characteristic values of components, particularly, temperature and pressure values in addition to information concerning turbine operation.
As described above, the SCADA system is focused on monitoring the current operating conditions of the wind turbines, whereas a condition monitoring system (CMS) is aimed at diagnosing malfunctions of wind turbines at an early stage and preventing breakdown thereof by more carefully monitoring, analyzing, and predicting conditions of wind turbine components, thereby enhancing reliability and economic feasibility of the wind turbines.
The condition monitoring system is mainly classified into a blade condition monitoring system, a component vibration condition monitoring system, and an oil condition monitoring system according to monitoring regions and diagnoses malfunctions in advance using a variety of advanced analysis techniques.
Like the SCADA system, the condition monitoring system performs monitoring, analysis, and reporting functions. However, the condition monitoring system is differentiated from the SCADA system in terms of monitoring target regions and analysis and prediction techniques.
As described above, the SCADA system and the condition monitoring system monitor malfunctions of individual wind turbine components, such as a blade, a gear box, and a generator and generate alarm messages by differentiating alarm levels (e.g., caution, warning, and alarm) according to a predicted degree of influence of the malfunctions.
However, a technique of determining malfunctions in terms of an entire wind turbine system instead of individual components is not yet applied to the SCADA system or the condition monitoring system.
A power curve showing wind turbine power versus wind speed in a graph form is a representative indicator representing turbine performance in terms of an entire wind turbine system, as an official performance assurance indicator of a wind turbine that has to be ensured by a turbine manufacturer.
FIGS. 1A-1G show power curves according to various malfunction examples occurring in a typical power turbine of a wind power generator.
Referring to FIGS. 1A-1G, power curves in various malfunction examples show that, in the case of malfunctions, there are power outputs departing from a normal power curve.
Due to the observation results, monitoring techniques based on a power curve attract the same attention as the condition monitoring system in the condition monitoring of a wind turbine. However, in spite of importance thereof, studies on the power curve monitoring techniques are not being actively conducted yet.
FIG. 2 is a graph showing an example of setting power curve limits measured in ISET according to a typical power curve monitoring technique.
Referring to FIG. 2, in a case study conducted at the Institute for Solar Energy Supply Technology (ISET) in Germany, alarm limits were set by classifying mean value data of power measured for five minutes into bins of a wind speed of 0.5 m/s, followed by calculating a mean value and a standard deviation for each bin.
However, in the method proposed by the ISET, a distance between upper and lower limits is increased with increasing wind speed higher than the rated wind speed to fit a stall-control turbine selected as an application target, thereby making it difficult to apply the method to mainly used pitch control in large-scale wind turbines.
FIGS. 3a and 3b are graphs showing examples of setting power curve limits measured in the intelligent system laboratory according to a typical power curve monitoring technique.
Referring to FIGS. 3a and 3b, a representative power curve monitoring technique is a non-parametric model technique using a data mining algorithm, developed by the Intelligent Systems Lab (Professor Andrew Kusiak) at the University of Iowa in the USA.
The Intelligent Systems Lab tested various data mining algorithms for power curve estimation. Thereamong, k-NN model exhibited the most excellent performance. In addition, the intelligent systems lab proposed the residual control chart technique for setting power curve limits.
However, accuracy of the k-NN model of the data mining technique proposed by the Intelligent Systems Lab can be significantly reduced by faulty data. In addition, each time new data is given, k pieces of adjacent data have to be selected by calculating distances between the new data and all learning data. Therefore, it takes more time to calculate the distances therebetween as the amount of learning data increases.
The related art is disclosed in Korean Patent Publication No. 10-2010-0031897 (entitled “Device for Monitoring Wind Power Generator”).